Abstract

Background

Structural brain abnormalities have been described in individuals with an
at-risk mental state for psychosis. However, the neuroanatomical underpinnings
of the early and late at-risk mental state relative to clinical outcome remain
unclear.

Aims

To investigate grey matter volume abnormalities in participants in a
putatively early or late at-risk mental state relative to their prospective
clinical outcome.

Method

Voxel-based morphometry of magnetic resonance imaging data from 20 people
with a putatively early at-risk mental state (ARMS–E group) and 26
people with a late at-risk mental state (ARMS–L group) as well as from
15 participants with at-risk mental states with subsequent disease transition
(ARMS–T group) and 18 participants without subsequent disease transition
(ARMS–NT group) were compared with 75 healthy volunteers.

Results

Compared with healthy controls, ARMS–L participants had grey matter
volume losses in frontotemporolimbic structures. Participants in the
ARMS–E group showed bilateral temporolimbic alterations and subtle
prefrontal abnormalities. Participants in the ARMS–T group had
prefrontal alterations relative to those in the ARMS–NT group and in the
healthy controls that overlapped with the findings in the ARMS–L
group.

Conclusions

Brain alterations associated with the early at-risk mental state may relate
to an elevated susceptibility to psychosis, whereas alterations underlying the
late at-risk mental state may indicate a subsequent transition to
psychosis.

Neuroimaging studies of people at risk of developing psychosis have
provided evidence that the schizophrenia prodrome is associated with subtle
structural brain alterations in frontal, limbic and perisylvian brain
regions1–8
that may also be involved in the neurobiology of full-blown
schizophrenia.9–11
Genetic high-risk studies revealed neuroanatomical anomalies in the medial
temporal lobe structures, the anterior cingulate cortex as well as the
prefrontal cortex of asymptomatic individuals with a positive familial history
of psychotic illness. These alterations may represent genetically mediated
trait markers of the neurobiological vulnerability to the
disease.6,8,12

Prodromal research has increasingly focused on ultra-high-risk populations
defined by sets of risk factors combining prodromal symptoms, declining
functioning and traditional genetic high-risk criteria. Following this ‘
close-in’
strategy,13
structural alterations were identified in the temporal lobe, the anterior
cingulate cortex and
cerebellum.2,3,5
Moreover, these patterns and the time course of their development could be
further differentiated according to the ultra-high-risk individuals’
prodromal state and outcome: ultra-high-risk individuals with psychotic
symptoms without subsequent disease manifestation may have exclusive temporal
and limbic grey matter volume reductions over time compared with non-psychotic
ultra-high-risk
individuals,4 and
subsequent disease transition may be associated with additional longitudinal
volume reductions in limbic, temporal and cerebellar regions compared with
non-transition.4,5
Furthermore, ultra-high-risk individuals with a subsequent disease
manifestation may have alterations in cingulate, limbic, perisylvian and
intrasylvian structures already at
baseline.2,3,5

These findings suggest that pre-existing brain anomalies promote a
pathophysiological process leading to accumulating brain alterations in
parallel with the emergence of prodromal symptoms (see Pantelis et
al14 for
review): initially, these symptoms may appear as subtle
cognitive–perceptive ‘basic symptoms’ distinguishing the
early prodromal stage of psychosis from mild depressive syndromes and
indicating an elevated risk of a later disease
manifestation.15–18
Subsequently, attenuated psychotic symptoms and brief limited psychotic
symptoms (BLIPS) may hallmark the late prodromal stage, which is characterised
by a much higher, imminent risk of disease
transition.19–21

This prodromal concept has been challenged by a considerable overlap
between prodromal symptoms and psychopathological phenomena found in the
general
population,22,23
as well as by the absence of an ultimate disease transition in a significant
proportion of ultra-high-risk individuals. Similarly, it is unclear which
neurobiological abnormalities may be accurate predictors or just vulnerability
markers of psychosis: Phillips et
al24 found
hippocampal volume reductions in non-psychotic ultra-high-risk individuals
compared with healthy controls, but no such alterations in ultra-high-risk
individuals with psychotic symptoms and subsequent disease transition.
Borgwardt et
al2,3
detected volume increments in the left parahippocampal, fusiform and
perisylvian regions of ultra-high-risk individuals who later developed
schizophrenia compared with those who did not. These findings raise the
question of possible neuroplastic brain changes around the time of disease
onset.24,25

Recent prospective research into the neurobiological differences of
high-risk participants suspected to be in an early or late at-risk mental
state for psychosis based on established at-risk mental state criteria, which
combined the basic symptom
concept26 with the
Personal Assessment and Crisis Evaluation (PACE)
criteria,20
described significant associations between an increased symptomatological
proximity to overt psychosis and: reduced hippocampal
volumes;27 a
sensorimotor gating
deficit;28 and
decreased amplitudes of auditory evoked P300
potentials.29
Within this context, we used the identical at-risk mental state criteria
together with voxel-based morphometry (VBM) in order to: investigate
structural brain differences between participants in a putative early at-risk
mental state (ARMS–E group) or late at-risk mental state (ARMS–L
group) for psychosis; and to delineate which of these differences were
associated with a later disease manifestation by comparing those at-risk
mental state participants with subsequent transition to psychosis
(ARMS–T group) with those without transition (ARMS–NT group). The
at-risk mental state samples were compared directly and relative to matched
healthy individuals. Based on the previous literature of structural brain
abnormalities in the at-risk mental state and in established
psychosis,1–6,9,10,27,30,31
we hypothesised: that the ARMS–E and ARMS–L samples could be
differentiated according to the spatial extent and magnitude of alterations
within the prefrontal cortex, the language-related perisylvian structures, the
limbic system and the cerebellum; and that distinct alterations would be
present in the prefrontal, perisylvian and limbic structures of ARMS–T
v. ARMS–NT participants.

Method

Participants

Forty-six at-risk mental state participants, including 20 ARMS–E and
26 ARMS–L individuals (Table
1), were recruited at the Early Detection and Intervention Centre
for Mental Crises (FETZ) of the Department of Psychiatry and Psychotherapy,
Ludwig-Maximilians-University, Germany. The FETZ participated in a prospective
high-risk multicentre study within the German Research Network on
Schizophrenia
(GRNS).32 Potential
at-risk mental state participants were referred to the FETZ by primary
healthcare services and were examined using a standardised inclusion criteria
checklist (ICC) with operationalised definitions of different types of
prodromal symptoms: basic symptoms (Appendix) taken from the Bonn Scale for
Assessment of Prodromal Symptoms
(BSABS);26,33
attenuated psychotic symptoms; and BLIPS as defined by the PACE criteria
(Appendix).20,21
The following recruitment criteria were identically implemented in all
participating sites of the multicentre GRNS project and were employed by
previous
studies.27–29,34

Potential participants with an at-risk mental state meeting defined sets of
state and/or trait markers were included in the study. Inclusion based on
global functioning and trait factors required a >30 point reduction in the
DSM–IV Global Assessment of Functioning
(GAF)35 scale score
and either a familial history of psychotic disorders in the first-degree
relatives, or a personal history of pre-/perinatal complications. Inclusion
based on psychopathological state markers required ≥1 positive item in the
basic symptoms, attenuated psychotic symptoms or BLIPS categories of the ICC.
The at-risk mental state participants were divided into two samples according
to their symptomatological proximity to psychosis based on the presence and
absence of specific psychopathological state criteria. This two-stage
conceptualisation of the at-risk mental state distinguished between a
putatively early, or non-psychotic, at-risk mental state, with increased but
not imminent risk of psychosis and a putatively late, or psychotic, at-risk
mental state, with a higher or imminent risk of
psychosis.16,27–29,36

The ARMS–E group consisted of participants without attenuated
psychotic symptoms and BLIPS, who had had ≥1 basic symptom (Appendix) on
several occasions within the past 3 months and appearing first at least 12
months prior to study inclusion and/or who met a global functioning and trait
criterion (see above). In line with the PACE
criteria,19 the
ARMS–L sample comprised individuals with ≥1 attenuated psychotic
symptom within the past 3 months, appearing several times per week and/or with ≥
1 BLIPS, spontaneously resolving within 1 week. Basic symptoms and/or
global functioning and trait criteria markers were not exclusion criteria for
this sample.

a past or present diagnosis of schizophrenia-spectrum or bipolar disorders,
as well as delirium, dementia, amnestic or other cognitive disorders, mental
retardation and psychiatric disorders due to a somatic factor or related to
psychotropic substances, following the
DSM–IV35
criteria;

alcohol or drug abuse according to DSM–IV within 3 months prior to
examination; and

past or present inflammatory, traumatic or epileptic diseases of the
central nervous system.

At study inclusion, the personal and familial history was obtained using a
semi-structured clinical interview, which covered pre- and perinatal
complications, developmental abnormalities during childhood and adolescence,
past or present somatic diseases and psychiatric conditions, previous or
current medications, nicotine, alcohol and drug use, as well as socioeconomic
status. The premorbid IQ of the at-risk mental state participants was assessed
using the Mehrfachwahl-Wortschatz-Intelligenztest (MWT–B), an
established instrument in German-speaking
populations.37
Psychopathology was additionally rated with the Positive and Negative Syndrome
Scale (PANSS)38 and
Montgomery–Åsberg Depression Scales
(MADRS).39

A regular clinical follow-up was performed at monthly intervals during the
first year and quarterly in the following 3 years. At each assessment,
participants were re-evaluated using the ICC in order to detect shifts in the
prodromal symptomatology towards a different at-risk mental state or a
possible transition to
psychosis.19 In
participants meeting the transition criteria the diagnosis of
schizophrenia-spectrum disorders was determined using the
ICD–1040
diagnostic research criteria at time of transition and after 1 year.
Thirty-three people in the ARMS groups (13 ARMS–E, 20 ARMS–L)
completed the 4-year follow-up, of whom 15 developed psychosis (ARMS–T:
1 ARMS–E, 14 ARMS–L). The mean time to transition was 188 days
(range 35–777) for the entire ARMS–T group and 142 days (range
35–642) for the 14 ARMS–L participants. One individual in the
ARMS–E group developed psychosis after 777 days. The ICD–10
diagnoses were schizophrenia (n = 9), schizoaffective psychosis
(n = 5) and schizotypal disorder (n = 1). Six participants
did not finish follow-up and seven dropped out from the study as they refused
to participate or because they could not be contacted. No participants
received antipsychotic agents prior to magnetic resonance imaging (MRI) and
clinical examination.

Seventy-five healthy controls matched group-wise for age, gender,
handedness and educational years to the entire ARMS group were recruited for
MRI examination and assessed at scan time with the same standardised clinical
interview as the ARMS participants (Table
1). Only those healthy volunteers were included that had no
personal or familial history (first-degree relatives) of neurological and/or
psychiatric conditions. All the control group and participants in the ARMS
group provided their written informed consent prior to study inclusion. The
study was approved by the Local Research Ethics Committee of the
Ludwig-Maximilians-University.

MRI data pre-processing

The VBM5 toolbox
(http://dbm.neuro.uni-jena.de),
an extension of SPM5, was used to segment the images into grey matter, white
matter and cerebrospinal fluid (CSF) tissue maps and to normalise these maps
to the standard space defined by the anatomical template of the Montreal
Neurological Institute (MNI–152;
www.bic.mni.mcgill.ca/cgi/icbm_view).
This pre-processing protocol has been described
previously.1 In
summary, the VBM5 toolbox provides several enhancements compared with the
standard SPM5 algorithms as it combines the unified segmentation approach of
SPM541 with a
hidden Markov field (HMRF)
model42 in order to
optimise the quality of tissue segmentation by increasing the signal-to-noise
ratio of the data. Furthermore, the present study utilised the toolbox’s
option of writing the normalised tissue maps without making use of the a
priori knowledge of the ICBM (International Consortium for Brain Mapping)
tissue priors. These tissue priors are derived from the brains of healthy
participants and may therefore introduce a segmentation bias in the final
tissue maps of patient populations that may deviate anatomically from the
healthy controls. In the current study, the use of this option led to a
significantly better delineation of fine sulcal and gyral cortical folding
compared with the classical statistical parametric mapping (SPM) approach.

Global grey matter, white matter, CSF and total intracranial volumes were
computed using the native-space tissue maps of each participant. Moreover, the
anatomical heterogeneity was compared between samples by calculating the
squared distance of each person’s modulated, normalised grey matter
tissue map to the sample mean using the VBM5 toolbox
(Table 1). Data pre-processing
was finished: by proportionally scaling each person’s modulated,
normalised grey matter tissue map to the respective global grey matter volume
in order to remove the effects of global brain size differences on local brain
structures; and by applying an isotropic 10 mm full-width-at-half-maximum
Gaussian filter to the scaled grey matter tissue maps.

Statistical analysis

Within the framework of the general linear model two analyses of covariance
(ANCOVA) were designed in order to investigate focal grey matter volume
differences between the control group, ARMS–E group and ARMS–L
group (design 1) and the control group, ARMS–NT group and ARMS–T
group (design 2). Age and gender were entered as nuisance regressors in the
statistical designs in order to regress out possible effects of these
parameters on between-group grey matter volume differences. Statistical
inference was performed at the cluster-level by assessing the SPM{t}
images using the non-stationary random field theory described by Hayasaka
et al43
and applied in Meisenzahl et
al.1

Statistical inference started with the definition of a primary threshold in
order to identify contiguous voxels for the cluster-level analysis at a
relatively lenient voxel level of P<0.01,
uncorrected.1 Then,
a family-wise error (FWE) corrected cluster-size
threshold44 of
P<0.05 was applied, producing a spatial extent threshold of 5.4
cm3 in design 1 and 5.2 cm3 in design 2. Finally,
cluster sizes were adjusted for smoothness non-uniformity by means of the VBM5
toolbox. Anatomical regions covered by significant clusters were identified
using automated anatomical
labeling.45 Grey
matter volume differences in these regions were quantified by calculating the
effect sizes (Cohen’s D) of the SPM{t} maps and by
extracting the percentage between-group differences (% difference) from the
contrast images (see online Table DS4).

Statistical inference of between-group differences was performed as
follows: for design 1, grey matter volume differences (decreases, increases)
were assessed using T contrasts between the ARMS–E group and
the control group ([control group> ARMS–E group], [control
group<ARMS–E group]) and between the ARMS–L group and the
control group ([control group> ARMS–L group], [control
group<ARMS–L group]). Then, grey matter differences (decreases,
increases) were directly examined between the ARMS–E group and the
ARMS–L group ([ARMS–E group>ARMS–L group], [ARMS–E
group<ARMS–L group]). The analysis of grey matter differences in the
same way for design 2.

Owing to the relative gender imbalance between the ARMS–E group and
the ARMS–L group (Table
1), a supplementary VBM analysis was performed in order to
investigate possible gender effects on the between-group differences observed
in the [ARMS–E group >ARMS–L group] contrast. Therefore, a
two-factorial ANCOVA was constructed with gender and ARMS group entered as
factors and age defined as the nuisance covariate. Interactions between grey
matter volume reductions in ARMS–L group v. ARMS–E group
and (1) male ARMS v. female ARMS, or (2) female ARMS v. male
ARMS were evaluated at the cluster-level threshold of P<0.05,
FWE-corrected using [male ARMS < female ARMS] × [ARMS–E group >
ARMS–L group] and [male ARMS > female ARMS] ×
[ARMS–E group > ARMS–L group] contrasts.

The different spatial extents of the frontal clusters detected by the
[control group>ARMS–L group] contrast and the [ARMS–E
group>ARMS–L group] contrast (see online Fig. DS1) pointed to subtle
frontal grey matter volume abnormalities in the ARMS–E group that did
not reach significance in the [control group> ARMS–E group] contrast.
Therefore, correlations between grey matter volume and increasing
symptomatological proximity to psychosis were assessed in a supplementary
general linear model design that modelled the symptomatological proximity as a
three-level gradation by assigning values of 3, 2 and 1 to the control group,
ARMS–E group and ARMS–L group respectively. Age and gender were
entered as nuisance regressors in the statistical design. Two contrasts tested
for positive [control group>ARMS–E group> ARMS–L group] and
negative [control group<ARMS–E group< ARMS–L group]
correlations following the same statistical inference strategy as described
above.

Results

Sociodemographic and clinical parameters

No significant differences were found between the control group,
ARMS–E and ARMS–L groups with respect to age, handedness and
educational years (Table 1).
The gender distribution between the ARMS–E and ARMS–L participants
was relatively unbalanced (ARMS–E group: 50% females; ARMS–L
group: 27% females), but not significantly different between groups
(χ2 = 2.59, P = 0.274). No significant
sociodemographic differences were found between the control, ARMS–NT and
ARMS–T groups, except for age (F = 3.16, P =
0.048).

The premorbid IQ was neither significantly different in the ARMS–E
group v. ARMS–L group, nor in ARMS–NT group v.
ARMS–T group. Reduced global functioning did not differ between the
ARMS–E and ARMS–L groups, but all 15 ARMS–T participants
showed a GAF reduction of >30 points at study inclusion compared with 55.6%
in the ARMS–NT group. The ARMS groups were not significantly different
with respect to the prevalence of schizophrenic or affective psychosis in the
first-degree relatives or pre-/perinatal complications. No significant
differences were detected between the ARMS–E and ARMS–L groups
regarding PANSS and MADRS scores. The ARMS–T group scored significantly
higher in the PANSS positive symptoms score and showed a trend towards a lower
total MADRS score. The overall prevalence of basic symptoms was higher in the
ARMS–L group v. ARMS–E group and the ARMS–T group
v. ARMS–NT group. The ARMS–T participants showed a
significantly higher prevalence of attenuated psychotic symptoms and BLIPS
compared with the ARMS–NT participants at baseline.

Global anatomical parameters

No significant differences between the control, ARMS–E and
ARMS–L groups, as well as the control, ARMS–NT and ARMS–T
groups were found regarding global grey matter, white matter, CSF, total
intracranial volumes and anatomical heterogeneity
(Table 1.)

VBM analysis: control v. ARMS–E v. ARMS–L groups

In the VBM analysis of the control v. ARMS–E v.
ARMS–L groups (online Fig. DS1 and online Tables DS1 and DS2) no
significant grey matter volume increments were observed in the ARMS–E
group v. the control group, ARMS–L group v. control
group and ARMS–E group v. ARMS–L group.

[Control group>ARMS–L group] contrast

the frontal interhemispheric area spanning the dorsomedial and ventromedial
prefrontal cortex as well as the olfactory cortices and extending into the
anterior cingulate cortex and the caudate nucleus, bilaterally;

the lateral prefrontal areas, including the dorsolateral and ventrolateral
prefrontal cortex, with an extension into the left anterior insula and
stretching bilaterally from the frontopolar regions to the supplementary motor
areas and precentral gyri; and

the orbitofrontal areas, reaching from the medial to the lateral
orbitofrontal cortex.

the right medial temporal lobe (amygdala, hippocampus and parahippocampus),
including the fusiform gyrus;

the frontal interhemispheric region occupying portions of the anterior
cingulate cortex, caudate and thalamus; and finally

within the right-hemispheric medial parietal cortex and precuneus.

Effect sizes were medium to high (0.5–0.9) with maximum effects
within the left dorsomedial prefrontal cortex and the right dorsolateral
prefrontal cortex. The largest percentage volume reductions were observed in
the right anterior cingulate cortex (6.7%) and in adjacent parts of the left
Broca’s area and precentral cortex (7.1%).

[ARMS–E group>ARMS–L group] contrast

This contrast identified similar, but less extended frontal clusters of
grey matter volume reductions compared with the [control group>ARMS–L
group] contrast. The effect sizes were medium to high with maxima in the left
subgenual anterior cingulate cortex as well as in the ventromedial prefrontal
cortex and dorsomedial prefrontal cortex, bilaterally. The percentage grey
matter volume reductions in ARMS–L participants relative to ARMS–E
participants were comparable to the [control group< ARMS–L group]
contrast, with maxima in the anterior cingulate cortex, bilaterally.

VBM analysis: control v. ARMS–NT v. ARMS–T groups

In the VBM analysis of control v. ARMS–NT v.
ARMS–T groups (online Fig. DS2 and online Tables DS3 and DS4) no
significant grey matter volume increments were observed in the following
groups: ARMS–NT v. control, ARMS–T v. control
and ARMS–T v. ARMS–NT.

[Control group>ARMS–NT group] contrast

This contrast detected three clusters of grey matter volume reductions that
covered parts of the left and right dorsolateral prefrontal cortex, as well as
the precentral, postcentral and supramarginal gyri (left cluster:
kc = 10 925, PFWE<0.001, right
cluster: kc = 20 174, PFWE<0.001)
as well as the right medial and lateral temporal lobe structures, including
the amygdala, hippocampus, parahippocampus, superior temporal gyrus, middle
and inferior temporal, fusiform and lingual gyri (kc = 22
017, PFWE<0.001). Across these regions the maximum
effect sizes ranged from 0.5 (right Broca’s area) to 1.0 (right
precentral and left postcentral gyri). The maximum percentage differences were
detected in the border region between the dorsolateral prefrontal cortex and
the precentral gyrus (% difference 10.4) as well as in the superior temporal
sulcus (% difference 8.1).

[Control group>ARMS–T group] contrast

A frontal cluster of grey matter volume losses (kc =
124078, PFWE<0.001) was observed in the ARMS–T
group compared with the control group. Its spatial localisation was similar to
the cluster found in the [control group>ARMS–L group] contrast and
involved predominantly the right dorsolateral prefrontal cortex, ventrolateral
prefrontal cortex and large portions of the dorsomedial prefrontal cortex,
ventromedial prefrontal cortex and orbitofrontal cortex, bilaterally. The
largest effect sizes and percentage differences were detected within the right
anterior cingulate cortex (D = 1.0, % difference 10.2) and
dorsomedial prefrontal cortex (D = 1.0, % difference 9.0).

The overlap between these clusters and the clusters detected by the
[ARMS–E group>ARMS–L group] contrast was spatially confined to
small border regions between the dorsolateral prefrontal cortex and
ventrolateral prefrontal cortex, bilaterally.

Correlations between grey matter volume and symptomatological
proximity to psychosis

No significant negative correlations between grey matter volume and
symptomatological proximity to psychosis were detected (online Fig. DS4).
Positive correlations emerged in a pattern of anatomical regions previously
described in the [control group> ARMS–L group] contrast. Six clusters
occupied:

large bilateral portions of the prefrontal and orbitofrontal regions with
extensions into the cingulate and precentral cortices, the thalamus, left
insula and caudate nuclei (kc = 121 860,
PFWE<0.001);

Discussion

Prefrontal abnormalities and their relation to clinical outcome

Our first hypothesis, that the ARMS–L participants could be
distinguished from the ARMS–E group based on the extent and magnitude of
brain abnormalities was confirmed. The most pronounced alterations were found
in the ARMS–L group, with extended volume losses spanning the prefrontal
and orbitofrontal cortices and involving parts of the anterior cingulate
cortex, insula as well as medial and lateral temporal brain regions.
Furthermore, we detected grey matter volume abnormalities, which gradually
increased with the symptomatological proximity to psychosis (control group to
ARMS–E group to ARMS–L group) within the same pattern of brain
regions found to be altered in the ARMS–L group v. control
group. These findings are in keeping with previous studies of ultra-high-risk
participants and people with first-episode
schizophrenia.1,9,14
Studies involving participants from the PACE clinic in Melbourne reported
abnormal anterior cingulate cortex and paracingulate
morphology,46 as
well as progressive changes in the anterior cingulate cortex, orbitofrontal
cortex and ventrolateral prefrontal cortex of ultra-high-risk participants
with subsequent disease
manifestation.5 Data
from the genetically defined sample of the Edinburgh High Risk Study (EHRS)
revealed grey matter density reductions within the anterior cingulate cortex
and orbitofrontal cortex of 146 high-risk participants compared with 36
healthy controls.6
In this context, a careful interpretation of our results may be that emerging
frontal brain alterations parallel the growing risk of psychosis within the
framework of a late neurodevelopmental
process.47 This
process may represent the sequelae of brain anomalies acquired during the
prenatal phase and early childhood. It may be triggered during brain
maturation in early adulthood when higher-order cortical association areas are
placed ‘under functional
demand’.14,47
On the basis of a predisposing neurobiological vulnerability, this process may
result in progressive structural brain changes that occur around the time of
disease onset and primarily affect prefrontal as well as temporal cortical
regions.2–6,14
Alternatively, these brain abnormalities may be interpreted as a
neurobiological trait marker for a subgroup of at-risk mental state
participants characterised by a liability to attenuated or transient psychotic
symptoms.

To further differentiate between neuroanatomical markers of an elevated
susceptibility to psychosis and those linked to an ultimate disease
manifestation, we investigated grey matter abnormalities relative to the
clinical outcome of our two at-risk mental state samples. During the clinical
follow-up period of 4 years, 14 ARMS–L participants converted to
psychosis, whereas this happened only in 1 ARMS–E participant. The low
conversion rate of our ARMS–E group is inconsistent with the study of
Klosterkötter et
al,26 who
reported that 50% of their 160 at-risk mental state participants, who were
selected for having basic symptoms, converted to psychosis during a follow-up
period of 10 years. The discrepancy between these different conversion rates
may be because of:

the differences regarding the mean age at study inclusion, which was 29.7
years (s.d. = 11.4) in the study of Klosterkötter et
al;26 and

the larger proportion of female participants in the ARMS–E
v. ARMS–L group, potentially resulting in a later mean age at
disease onset in the ARMS–E
sample.48

Recent findings suggested that different types of prodromal states may
exist, with a substantial proportion (33%) of converters having prodromal
phases of more than 6
years.36 Therefore,
a longer follow-up period of an enlarged ARMS–E sample will provide a
more definite answer with respect to the conversion rate of these participants
and regarding the temporal sequence of at-risk mental states during the
prodromal phase of psychosis. Our current study suggests that our at-risk
mental state samples differ in their predictive power, and therefore represent
two levels of risk for an ultimate disease transition. This interpretation is
in line with the clinical risk model of Maier et
al,49 who
proposed a gradual development of the schizophrenia prodrome over several
stages that are characterised by an increasing symptomatological proximity to
full-blown schizophrenia and an increasing predictive power regarding an
ultimate disease transition. Within this concept, the early at-risk mental
state may be regarded as a precursor of psychosis marked by an elevated level
of vulnerability for the disease, whereas the late at-risk mental state may be
interpreted as a ‘real’ prodromal phase of psychosis because of
its considerable predictive validity.

In keeping with the study of Yung et
al,21 we found
that the ARMS–T participants experienced more frequently a significant
functional decline as well as a higher prevalence of basic symptoms,
attenuated psychotic symptoms and BLIPS as compared with the ARMS–NT
individuals. Consistent with our second hypothesis, prefrontal structural
alterations in the ARMS–T participants were identified on average 6
months prior to disease transition, which largely overlapped with the
abnormalities of the ARMS–L sample (online Fig. DS5). An additional
analysis of the quantitative differences between the ARMS–L and
ARMS–T samples revealed that the ARMS–T participants had more
pronounced prefrontal grey matter volume reductions compared with the entire
ARMS–L sample, with a maximum difference of 4.5% in the right anterior
cingulate cortex (online Fig. DS5). Moreover, we observed divergent patterns
of structural abnormalities in the ARMS–NT and ARMS–T participants
compared with the healthy controls: the prefrontal abnormalities detected in
the ARMS–NT group v. the control group were confined to the
dorsolateral prefrontal cortex, bilaterally, whereas only right medial and
lateral temporal lobe alterations were found only in the control group
v. ARMS–NT group. These findings are consistent with previous
studies reporting grey matter volume reductions in the anterior cingulate
cortex2,3
and progressive prefrontal grey matter losses in ARMS–T relative to
ARMS–NT
participants.47
Previous neuroimaging studies reported significant correlations between poor
neurocognitive measures and prefrontal cortex abnormalities in established
schizophrenia.50,51
In this context, neuropsychological data have shown that cognitive and
executive functioning are already impaired in at-risk mental state
participants prior to disease
onset,52 with more
severe impairments being associated with the late at-risk mental
state36 and a
further deterioration being linked to subsequent disease
manifestation.53
Based on these results and our own findings, we may cautiously interpret
prefrontal brain alterations pre-dating psychosis as a marker of clinical
outcome and not only as a marker of liability to attenuated or transient
psychotic symptoms.

Temporal lobe abnormalities in different at-risk mental states

In addition to the grey matter volume reductions commonly found in the
medial temporal lobe of both at-risk mental state groups, our main VBM
analysis revealed patterns of neuroanatomical abnormalities that seemed to
differ qualitatively between the two at-risk samples. Structural abnormalities
in the ARMS–E group did not involve the prefrontal or orbitofrontal
areas, but were restricted to the temporolimbic structures, bilaterally.
Conversely, the ARMS–L group showed structural anomalies in the left
superior temporal gyrus and in the prefrontal and orbitofrontal cortices
undetected by the ARMS–E v. control group comparison. Besides
these divergent patterns, our supplementary VBM analysis identified grey
matter volume abnormalities that gradually increased from the control group to
the ARMS–E group to the ARMS–L group within a frontotemporolimbic
pattern that was highly similar to the pattern detected by the ARMS–L
v. control groups contrast (online Fig DS4). Taken together, these
findings suggest that:

basic symptoms define a risk level of psychosis that is not only associated
with medial and lateral temporal lobe abnormalities, but also with subtle
perisylvian, prefrontal, parietal, thalamic and cerebellar anomalies; and

attenuated psychotic symptoms and/or BLIPS mark a higher level of risk
characterised by more pronounced structural anomalies within these
regions.

Alterations of the medial temporal lobe regions were previously reported in
individuals with manifest
schizophrenia9,10,54,55
and people with an at-risk mental
state.2,3,5,6
In this context, Seidman et
al7 discussed
limbic abnormalities as a crucial vulnerability indicator of psychosis that
may be associated with impaired verbal declarative memory functions in
individuals with an at-risk mental state. In line with these data, Job et
al4 reported
exclusive longitudinal grey matter density losses in the medial, but also
lateral temporal lobe regions (superior temporal gyrus, inferior temporal
gyrus) of people with an asymptomatic at-risk mental state who later developed
transient or isolated psychotic symptoms. Moreover, they found further
exclusive temporolimbic grey matter density losses in those participants who
subsequently developed schizophrenia.

The non-reduction of left hippocampal volume found in the ARMS–L
group v. control group is consistent with the results of Phillips
et al,24
who reported a similar finding in 20 ultra-high-risk individuals with
psychotic symptoms v. 40 non-psychotic ultra-high-risk individuals.
The authors also detected associations between a larger hippocampus at
baseline and subsequent disease transition. Moreover, recent EHRS studies
revealed positive correlations between the grey matter density of the superior
temporal gyrus and productive symptoms in at-risk mental state
participants,56,57
which is in contrast to the negative correlations between temporal brain
volumes and positive symptoms of individuals with manifest
schizophrenia.58,59
Finally, Borgwardt et
al2 identified
grey matter volume increments in 12 ARMS–T v. 23 ARMS–NT
participants, which were bilaterally localised in the parahippocampus,
thalamus as well as the occipital, temporal and parietal brain regions.

These findings may point to a complex pattern of brain abnormalities
underlying different vulnerability levels of psychosis that involve not only
volume reductions, but also volume increments within interconnected cortical
and subcortical
structures.57 In
the context of these findings, our cross-sectional and correlational VBM
findings may be cautiously interpreted within the framework of a late
neurodevelopmental
process14,47
that results in progressive volumetric declines within frontotemporolimbic
brain structures, but that also leads to the transient normalisation of
distinct cortical regions (left hippocampus) around the time of disease onset.
Alternatively, these grey matter alterations may be interpreted as
long-standing neuroanatomical patterns that pre-date the onset of prodromal
symptoms and represent trait markers of different levels of vulnerability to
psychosis.

Limitations and implications

The definition of the at-risk mental state groups followed a two-stage
conceptualisation of the prodrome that distinguishes between putatively early
and late prodromal
stages.32 As
discussed above, the low predictive validity of our ARMS–E group
regarding a subsequent transition to psychosis questions the hypothetical
prodromal syndromic sequence following a single trajectory of ‘
unspecific symptoms to predictive basic symptoms to attenuated
psychotic symptoms to transient psychotic
symptoms’.60
Thus, we could not decide whether our observations in the putatively ‘
early’ and ‘late’ at-risk mental state groups
represent two neurobiological cut-outs from a longitudinal course of brain
changes, leading ultimately to the manifestation of overt psychosis, or
whether they represent two differential risk levels for psychosis with
distinct, possibly long-standing neuroanatomical underpinnings. Nevertheless,
our finding of accumulating brain abnormalities being associated with an
increasing symptomatological proximity to psychosis is in keeping with
previous studies, which reported a deterioration of neurocognitive,
neurophysiological and neuroanatomical markers in similarly defined
individuals with an ‘early’ and a ‘late’ at-risk
mental
state.27–29
Future prospective studies combining repeated MRI and clinical examinations
may further disentangle how brain abnormalities pertaining to different
vulnerability states interact with different possible trajectories of emerging
psychosis.

Although we controlled for gender effects, we cannot completely rule out an
effect of the unbalanced gender distribution between the ARMS–E and
ARMS–L groups. However, the results of our group × gender analysis
overlapped with the ARMS–E group v. ARMS–L group findings
only in relatively small prefrontal areas, bilaterally. It is noteworthy that
the result of an abnormal sexual dimorphism modulating the structural
abnormalities in the ARMS–L group is partly consistent with previous MRI
studies61
investigating gender-mediated structural brain alterations in people with
established schizophrenia. Furthermore, this finding may be in keeping with a
stronger cognitive impairment observed in male v. female
participants.62–64
Larger at-risk mental state gender subgroups are needed to further elucidate
the impact of gender-mediated pathophysiological processes on the development
of cortical abnormalities during the at-risk mental state.

To our knowledge, this is the first study that characterised structural
brain abnormalities in an at-risk mental state sample selected for basic
symptoms. Furthermore, we employed the new unified segmentation algorithms of
SPM5 with the enhancements of the VBM toolbox to meet the criticism of
previous SPM
versions.65
Consistent with recent MRI
studies,2,3,5,66
we employed cluster-level inference to detect spatially contiguous, but subtle
abnormalities.

In summary, our findings support the hypothesis that structural changes
within the temporolimbic system may be present in a putatively ‘
early’ at-risk mental state. A higher level of susceptibility to
attenuated and/or transient psychotic symptoms may be associated with
prefrontal and orbitofrontal alterations. From the retrospective view of
clinical outcome, our findings suggest that prefrontal and orbitofrontal brain
abnormalities pre-date a subsequent disease manifestation. Finally, our data
may point to a complex, possibly dynamic pattern of frontotemporolimbic brain
alterations underlying an increasing vulnerability to psychosis.

Appendix

Inclusion criteria for the early at-risk mental state group (ARMS–E)
and late at-risk mental state group (ARMS–L) participants. Adopted from
Häfner et
al32

having at least one of the following attenuated positive symptoms within
the last 3 months, appearing several times per week for a period of at least 1
week:

ideas of reference

odd beliefs or magical thinking

unusual perceptual experiences

odd thinking and speech

suspiciousness or paranoid ideation

and/or

having at least one of the following BLIPS, defined as the appearance of
one of the following psychotic symptoms for less than 1 week (interval between
episodes at least 1 week), resolving spontaneously:

hallucinations

delusions

formal thought disorder

gross disorganised or catatonic behaviour.

Acknowledgments

We would like to thank Dr. Reinhold Bader, Linux Cluster Systems for the
Munich and Bavarian Universities, for his support in integrating the VBM5 and
SPM5 algorithms into the batch system of the Linux cluster.